A Novel Statistical Model to Estimate Host Genetic Effects Affecting Disease Transmission.
ABSTRACT: There is increasing recognition that genetic diversity can affect the spread of diseases, potentially affecting plant and livestock disease control as well as the emergence of human disease outbreaks. Nevertheless, even though computational tools can guide the control of infectious diseases, few epidemiological models can simultaneously accommodate the inherent individual heterogeneity in multiple infectious disease traits influencing disease transmission, such as the frequently modeled propensity to become infected and infectivity, which describes the host ability to transmit the infection to susceptible individuals. Furthermore, current quantitative genetic models fail to fully capture the heritable variation in host infectivity, mainly because they cannot accommodate the nonlinear infection dynamics underlying epidemiological data. We present in this article a novel statistical model and an inference method to estimate genetic parameters associated with both host susceptibility and infectivity. Our methodology combines quantitative genetic models of social interactions with stochastic processes to model the random, nonlinear, and dynamic nature of infections and uses adaptive Bayesian computational techniques to estimate the model parameters. Results using simulated epidemic data show that our model can accurately estimate heritabilities and genetic risks not only of susceptibility but also of infectivity, therefore exploring a trait whose heritable variation is currently ignored in disease genetics and can greatly influence the spread of infectious diseases. Our proposed methodology offers potential impacts in areas such as livestock disease control through selective breeding and also in predicting and controlling the emergence of disease outbreaks in human populations.
Project description:Infectious diseases have a huge impact on animal health, production and welfare, and human health. Understanding the role of host genetics in disease spread is important for developing disease control strategies that efficiently reduce infection incidence and risk of epidemics. While heritable variation in disease susceptibility has been targeted in livestock breeding, emerging evidence suggests that there is additional genetic variation in host infectivity, but the potential benefits of including infectivity into selection schemes are currently unknown. A Susceptible-Infected-Recovered epidemiological model incorporating polygenic genetic variation in both susceptibility and infectivity was combined with quantitative genetics selection theory to assess the non-linear impact of genetic selection on field measures of epidemic risk and severity. Response to 20 generations of selection was calculated in large simulated populations, exploring schemes differing in accuracy and intensity. Assuming moderate genetic variation in both traits, 50% selection on susceptibility required seven generations to reduce the basic reproductive number R<sub>0</sub> from 7.64 to the critical threshold of <1, below which epidemics die out. Adding infectivity in the selection objective accelerated the decline towards R<sub>0</sub>?<?1, to 3 generations. Our results show that although genetic selection on susceptibility reduces disease risk and prevalence, the additional gain from selection on infectivity accelerates disease eradication and reduces more efficiently the risk of new outbreaks, while it alleviates delays generated by unfavourable correlations. In conclusion, host infectivity was found to be an important trait to target in future genetic studies and breeding schemes, to help reducing the occurrence and impact of epidemics.
Project description:BACKGROUND:For infectious diseases, the probability that an animal gets infected depends on its own susceptibility, and on the number of infectious herd mates and their infectivity. Together with the duration of the infectious period, susceptibility and infectivity determine the basic reproduction ratio of the disease ([Formula: see text]). [Formula: see text] is the average number of secondary cases caused by a typical infectious individual in an otherwise uninfected population. An infectious disease dies out when [Formula: see text] is lower than 1. Thus, breeding strategies that aim at reducing disease prevalence should focus on reducing [Formula: see text], preferably to a value lower than 1. In animal breeding, however, [Formula: see text] has received little attention. Here, we estimate the additive genetic variance in host susceptibility, host infectivity, and [Formula: see text] for the endemic claw disease digital dermatitis (DD) in Holstein Friesian dairy cattle, and estimate genomic breeding values (GEBV) for these traits. We recorded DD disease status of both hind claws of 1513 cows from 12 Dutch dairy farms, every 2 weeks, 11 times. The genotype data consisted of 75,904 single nucleotide polymorphisms (SNPs) for 1401 of the cows. We modelled the probability that a cow got infected between recordings, and compared four generalized linear mixed models. All models included a genetic effect for susceptibility; Models 2 and 4 also included a genetic effect for infectivity, while Models 1 and 2 included a farm*period interaction. We corrected for variation in exposure to infectious herd mates via an offset. RESULTS:GEBV for [Formula: see text] from the model that included genetic effects for susceptibility only had an accuracy of?~?0.39 based on cross-validation between farms, which is very high given the limited amount of data and the complexity of the trait. Models with a genetic effect for infectivity showed a larger bias, but also a slightly higher accuracy of GEBV. Additive genetic standard deviation for [Formula: see text] was large, i.e.?~?1.17, while the mean [Formula: see text] was 2.36. CONCLUSIONS:GEBV for [Formula: see text] showed substantial variation. The mean [Formula: see text] was only about one genetic standard deviation greater than 1. These results suggest that lowering DD prevalence by selective breeding is promising.
Project description:Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which we may estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence does not change between monitoring rounds, resulting in an underestimation of the disease incidence. In this paper, we develop an incidence estimation model accounting for epidemic growth with monitoring rounds that sample varying incidence. We also show how to accommodate the asymptomatic period that is the characteristic of most diseases. For practical use, we produce an approximation of the model, which is subsequently shown to be accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations. The estimation model is made available as an online application. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.
Project description:Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a 'hurdle model' approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic 'complete' networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of 'fast' ( R0 = 3) and 'slow' ( R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.
Project description:The mechanisms underlying virus emergence are rarely well understood, making the appearance of outbreaks largely unpredictable. Bluetongue virus serotype 8 (BTV-8), an arthropod-borne virus of ruminants, emerged in livestock in northern Europe in 2006, spreading to most European countries by 2009 and causing losses of billions of euros. Although the outbreak was successfully controlled through vaccination by early 2010, puzzlingly, a closely related BTV-8 strain re-emerged in France in 2015, triggering a second outbreak that is still ongoing. The origin of this virus and the mechanisms underlying its re-emergence are unknown. Here, we performed phylogenetic analyses of 164 whole BTV-8 genomes sampled throughout the two outbreaks. We demonstrate consistent clock-like virus evolution during both epizootics but found negligible evolutionary change between them. We estimate that the ancestor of the second outbreak dates from the height of the first outbreak in 2008. This implies that the virus had not been replicating for multiple years prior to its re-emergence in 2015. Given the absence of any known natural mechanism that could explain BTV-8 persistence over this long period without replication, we hypothesise that the second outbreak could have been initiated by accidental exposure of livestock to frozen material contaminated with virus from approximately 2008. Our work highlights new targets for pathogen surveillance programmes in livestock and illustrates the power of genomic epidemiology to identify pathways of infectious disease emergence.
Project description:Gastrointestinal nematodes are a global cause of disease and death in humans, wildlife and livestock. Livestock infection has historically been controlled with anthelmintic drugs, but the development of resistance means that alternative controls are needed. The most promising alternatives are vaccination, nutritional supplementation and selective breeding, all of which act by enhancing the immune response. Currently, control planning is hampered by reliance on the faecal egg count (FEC), which suffers from low accuracy and a nonlinear and indirect relationship with infection intensity and host immune responses. We address this gap by using extensive parasitological, immunological and genetic data on the sheep-Teladorsagia circumcincta interaction to create an immunologically explicit model of infection dynamics in a sheep flock that links host genetic variation with variation in the two key immune responses to predict the observed parasitological measures. Using our model, we show that the immune responses are highly heritable and by comparing selective breeding based on low FECs versus high plasma IgA responses, we show that the immune markers are a much improved measure of host resistance. In summary, we have created a model of host-parasite infections that explicitly captures the development of the adaptive immune response and show that by integrating genetic, immunological and parasitological understanding we can identify new immune-based markers for diagnosis and control.
Project description:The world population is growing at a faster rate day-by-day and the demands for animal products are also increasing to meet the food security worldwide. For sustained production of animals products, healthy livestock and poultry farming are the major concerns as animals are susceptible to various infectious agents viz. bacteria, virus, and parasites leading to huge economical losses in the form of livestock’s morbidity and mortality. Besides, zoonotic nature of some infectious pathogens of animals is also raising concern for human safety. Vaccination of animals against various diseases present in different geographical regions is a best known strategy for prevention of different disease outbreaks both in organized and unorganized livestock and poultry sectors. Vaccines had played a major role in eradication of different dreaded diseases of livestock sectors globally. In this article we have discussed different vaccine types, various vaccine strategies used for the development of more efficacious and safe vaccines and commercially available vaccines for livestock and poultry.
Project description:Although farm biosecurity reduces the risk of disease spread among livestock, this knowledge is not always applied. Farmers' application of disease preventive measures is expected to depend on many things, e.g. whether they consider disease prevention possible and demographic factors. In this study, Swedish livestock farmers' perspectives on occurrence, control and communication related to infectious livestock diseases were investigated. A questionnaire study was performed in 2012-2013, and included responses from almost 2000 livestock farmers with cattle, pigs, sheep or goats. Associations between responses and factors related to herd type and demography were investigated using multivariable regression models. Results showed a strong general agreement among farmers that disease prevention is important. However, results also showed differing opinions among farmers. For example, female farmers indicated higher levels of perceived knowledge of disease spread and a stronger belief that they can prevent disease introduction. Results indicate that farmers who believe they have the necessary knowledge, have stronger sense of control and also demand that others take responsibility to prevent spread. Furthermore, dairy farmers were more likely to respond that repeated exposure to infections could be beneficial for animal health. The number of perceived disease outbreaks was also higher among these farmers. Regarding government issued compensation to farmers in case of outbreaks, a wide range of opinions were recorded. Responses confirm that the farm veterinarian is an important source of disease information and several different communication channels are needed to reach farmers. In conclusion, our results show that factors such as gender, education level and age influence how prevention and occurrence of disease outbreaks are perceived and best communicated. We suggest that efforts are made to increase knowledge about disease prevention among farmers and veterinary practitioners and that farm veterinarians should be encouraged to motivate farmers to strengthen farm biosecurity.
Project description:Measurements derived from neuroimaging data can serve as markers of disease and/or healthy development, are largely heritable, and have been increasingly utilized as (intermediate) phenotypes in genetic association studies. To date, imaging genetic studies have mostly focused on discovering isolated genetic effects, typically ignoring potential interactions with non-genetic variables such as disease risk factors, environmental exposures, and epigenetic markers. However, identifying significant interaction effects is critical for revealing the true relationship between genetic and phenotypic variables, and shedding light on disease mechanisms. In this paper, we present a general kernel machine based method for detecting effects of the interaction between multidimensional variable sets. This method can model the joint and epistatic effect of a collection of single nucleotide polymorphisms (SNPs), accommodate multiple factors that potentially moderate genetic influences, and test for nonlinear interactions between sets of variables in a flexible framework. As a demonstration of application, we applied the method to the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to detect the effects of the interactions between candidate Alzheimer's disease (AD) risk genes and a collection of cardiovascular disease (CVD) risk factors, on hippocampal volume measurements derived from structural brain magnetic resonance imaging (MRI) scans. Our method identified that two genes, CR1 and EPHA1, demonstrate significant interactions with CVD risk factors on hippocampal volume, suggesting that CR1 and EPHA1 may play a role in influencing AD-related neurodegeneration in the presence of CVD risks.
Project description:Whether a genetic informational nucleic acid is required for the infectivity of transmissible spongiform encephalopathies is central to the debate about the infectious agent. Here we report that an infectious prion formed with bacterially expressed recombinant prion protein plus synthetic polyriboadenylic acid and synthetic phospholipid 1-palmitoyl-2-oleoylphosphatidylglycerol is competent to infect cultured cells and cause prion disease in wild-type mice. Our results show that genetic informational RNA is not required for recombinant prion infectivity.